Preliminary data analyses, focused only on Grand Fir at this time

Contaminant ASVs were identified with the prevalence method using the decontam package.

The only taxa identified as contaminant sources in the negative controls were several species of Puccinia, Sebacina, and Glonium. These ASVs were removed from analyses.



Alpha-diversity exploration

Here’s a quick glance at alpha diversity for both bacterial and fungal ASVs, grouped by inoculum source:

Bacteria

ANOVA and Tukey Test indicated no significant difference in any of the alpha diversity metrics for bacteria.

Fungi

ANOVA and Tukey Test indicate that samples inoculated from “Inoculum Site 4” had several significant differences in fungal alpha diversity. Here are the pairings that were significantly different (along with the direction of the difference).



Aim 1:

Determine the ecological, phylogenetic, and network properties of root microbiomes that promote seedling performance

How do we define the concept of ‘promotes seedling performance?’

  • Leaf number
  • Leaf length
  • Wilting scale
  • Bud number
  • Shoot/root dry mass
  • Plant height

Hypotheses

  • H1: Guild abundance hypothesis: Seedling performance will be enhanced when root microbiomes have a greater proportion of microbial mutualists (e.g., ectomycorrhizal fungi) relative to saprotrophs, endophytes, or pathogens
  • H2: Microbial trait hypothesis: Seedling performance will be enhanced in root microbiomes that are enriched in taxa that confer tolerance to stress (e.g., Dove et al. 2022)
  • H3: Functional diversity: Seedling performance will be enhanced in root microbiomes comprised of taxa that represent high functional diversity
  • H4: Network connectivity: Seedling performance will be enhanced when root microbiomes contain taxa with high co-occurrence network connectivity



Ecological properties

Bacteria (16S | V6-V8)

  • Trait analysis (BacDive Database analysis slowly ongoing)

Fungi (ITS2)

How does the proportion of mutualist fungi relate to indicators of plant health?

In the following figure, the X-axis represents the proportion of fungi that could be identified to a mutualistic guild; the Y-axis represents the value of the various measures of plant success (facets). Y-axis scales vary between facets.

term estimate std.error statistic p.value
(Intercept) -0.336 0.084 -3.976 0.000
proportion_mutualist 0.581 0.182 3.191 0.001
proportion_mutualist:final_root_dm -0.086 0.214 -0.401 0.688
proportion_mutualist:height 0.014 0.212 0.068 0.946
proportion_mutualist:leaf_length -0.032 0.212 -0.149 0.881
proportion_mutualist:leaf_number 0.089 0.212 0.419 0.675
proportion_mutualist:shoot_dm -0.003 0.212 -0.016 0.987
proportion_mutualist:wilting_scale -0.309 0.212 -1.458 0.145

The proportion of mutualist fungal guild(s) in a given sample was a significant indicator of increased plant health, regardless of the health indicator measured.

We see an opposite pattern when looking at the proportions of putatively saprotrophic and pathogenic fungi

term estimate std.error statistic p.value
(Intercept) 0.134 0.047 2.844 0.005
proportion_saprotroph -0.890 0.356 -2.501 0.013
proportion_saprotroph:final_root_dm -0.009 0.538 -0.017 0.986
proportion_saprotroph:height -0.212 0.490 -0.432 0.666
proportion_saprotroph:leaf_length -0.152 0.490 -0.310 0.756
proportion_saprotroph:leaf_number -0.207 0.490 -0.422 0.673
proportion_saprotroph:shoot_dm -0.070 0.490 -0.143 0.887
proportion_saprotroph:wilting_scale 0.845 0.490 1.724 0.085
term estimate std.error statistic p.value
(Intercept) 0.113 0.047 2.429 0.015
proportion_pathogen -1.008 0.468 -2.155 0.032
proportion_pathogen:final_root_dm 0.208 0.737 0.283 0.778
proportion_pathogen:height -0.306 0.647 -0.473 0.637
proportion_pathogen:leaf_length -0.354 0.647 -0.547 0.584
proportion_pathogen:leaf_number -0.367 0.647 -0.568 0.570
proportion_pathogen:shoot_dm -0.093 0.647 -0.144 0.886
proportion_pathogen:wilting_scale 0.989 0.647 1.529 0.127

Functional potential is quite a bit trickier. The FungalTraits database is pretty sparse. You will see lots of missing data (represented as 0 for our purposes). Here, I’ve taken the number of known functionally important enzymatic pathways that have been noted in a given taxa, and scaled them by the observed relative abundance of that taxon in a given sample.

As you can see from all the x-axis 0’s, most taxa in this system had no real functional annotations available. This is probably swamping out any chance we have of detecting a signal.

term estimate std.error statistic p.value
(Intercept) 0.001 0.047 0.026 0.979
scaled_func_div 0.059 0.065 0.898 0.370
scaled_func_div:final_root_dm -0.091 0.092 -0.993 0.321
scaled_func_div:height -0.038 0.090 -0.419 0.676
scaled_func_div:leaf_length -0.091 0.090 -1.003 0.316
scaled_func_div:leaf_number 0.002 0.090 0.024 0.981
scaled_func_div:shoot_dm -0.081 0.090 -0.895 0.371
scaled_func_div:wilting_scale -0.124 0.090 -1.375 0.170

Nothing worth noting here… :(


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Phylogenetic properties

Bacteria (16S | V6-V8)

  • Phylogenetic dispersion

Fungi (ITS2)

  • Phylogenetic dispersion is tricky with ITS markers :(


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Network properties

Bacteria (16S | V6-V8)

Bacterial network properties do not really vary much between samples that had different inoculum sources.

Fungi (ITS2)

In general, the fungal networks were much less connected than bacterial networks, but connectivity varied widely between samples treated with different inoculum sources.

Samples inoculated from sites 1 and 4 have significantly stronger co-ocurrence networks.

Cross-Domain

In cross-domain networks, samples inoculated from inoculum source 3, display significantly more connectivity.


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Aim 2:

Determine how microbial transplants impact root microbiome community development and seedling performance in post-wildfire soils (Questions in red not addressed here…yet)

  • Inocula source: How does source of inocula impact root microbiome community development and seedling performance in post-wildfire soil?
  • Priority effects: How do priority effects impact the development of the root microbiome and conifer seedling performance in post-wildfire soils?
  • Resident soil biota: How does the resident soil microbial community modulate the impact of the donor microbial community on the root and rhizosphere microbiome and on plant performance?
  • Environmental conditions: How do soil properties (e.g., nutrient availability, moisture, pH, texture) modulate the impacts of the soil microbiome on plant performance in post wildfire soils?

Inoculum impact on microbial community

Bacteria (16S | V6-V8)

Inoculum source ‘burn frequency’ is nested within inoculum ‘site.’

1 2 3 4 5 6 Sterile
0 12 12 0 0 0 0 0
1 0 0 12 12 0 0 0
3 0 0 0 0 12 12 0
Sterile 0 0 0 0 0 0 12
a rows are burn freq., cols are sites, indicating sample counts in each combination

But here are NMDS ordinations of bacterial community, colored by inoculum properties:

PermANOVA with formula Community ~ inoculum site * inoculum burnfreq:

term df SumOfSqs R2 statistic p.value
inoculum_site 6 8.881229 0.30147 5.538582 0.001
Residual 77 20.578510 0.69853 NA NA
Total 83 29.459739 1.00000 NA NA
a Inoculum source explains ~ 30% of bacterial community structure

Same ordination, but with soil variables overlaid:

A Heatmap of bacterial ASVs with at least 10% total relative abundance (across all samples), split by inoculum source:


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Fungi (ITS2)

PermANOVA with formula Community ~ inoculum site * inoculum burnfreq:

term df SumOfSqs R2 statistic p.value
inoculum_burn_freq 3 6.281114 0.1666772 5.793262 0.001
inoculum_site 3 3.575126 0.0948704 3.297447 0.001
Residual 77 27.828065 0.7384524 NA NA
Total 83 37.684306 1.0000000 NA NA
a Inoculum source explains ~ 26% of fungal community structure

Same ordination, but with soil variables overlaid:

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A Heatmap of Fungal ASVs with at least 10% total relative abundance (across all samples), split by inoculum source:


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Inoculum impact on seedling performance

Bacteria (16S | V6-V8)

How were various plant traits affected by inoculum source?

(model outputs below - These are the same for fungi, since the predictor was simply inoculum source, not the actual community)

Effect of inoculum source on leaf number:

term estimate std.error statistic p.value
(Intercept) 31.083333 6.041336 5.1451089 0.0000020
inoculum_site2 8.500000 8.543740 0.9948805 0.3229106
inoculum_site3 -16.083333 8.543740 -1.8824699 0.0635499
inoculum_site4 18.833333 8.543740 2.2043430 0.0304869
inoculum_site5 4.166667 8.543740 0.4876865 0.6271576
inoculum_site6 11.916667 8.543740 1.3947834 0.1670919
inoculum_siteSterile -3.166667 8.543740 -0.3706417 0.7119221

Effect of inoculum source on plant height:

term estimate std.error statistic p.value
(Intercept) 5.6916667 0.3339915 17.0413517 0.0000000
inoculum_site2 -0.1083333 0.4723353 -0.2293568 0.8191997
inoculum_site3 -0.9500000 0.4723353 -2.0112830 0.0477952
inoculum_site4 0.9833333 0.4723353 2.0818544 0.0406783
inoculum_site5 0.4000000 0.4723353 0.8468560 0.3996998
inoculum_site6 -0.1833333 0.4723353 -0.3881423 0.6989817
inoculum_siteSterile -0.7083333 0.4723353 -1.4996409 0.1377966

Effect of inoculum source on shoot mass:

term estimate std.error statistic p.value
(Intercept) 0.1644667 0.0338152 4.8636897 0.0000060
inoculum_site2 0.0414250 0.0478219 0.8662345 0.3890527
inoculum_site3 -0.0793750 0.0478219 -1.6598036 0.1010216
inoculum_site4 0.1402917 0.0478219 2.9336266 0.0044120
inoculum_site5 0.0423333 0.0478219 0.8852286 0.3787894
inoculum_site6 0.0295833 0.0478219 0.6186145 0.5379961
inoculum_siteSterile -0.0534667 0.0478219 -1.1180367 0.2670274

Effect of inoculum source on root mass:

term estimate std.error statistic p.value
(Intercept) 0.3610600 0.0604655 5.9713393 0.0000001
inoculum_site2 0.0442483 0.0818707 0.5404662 0.5906159
inoculum_site3 -0.2197300 0.0855111 -2.5696071 0.0123453
inoculum_site4 0.2946855 0.0835451 3.5272624 0.0007515
inoculum_site5 0.0474900 0.0818707 0.5800612 0.5637628
inoculum_site6 -0.0297600 0.0835451 -0.3562148 0.7227666
inoculum_siteSterile -0.1637700 0.0855111 -1.9151894 0.0596140

Is there something special about inoculum site 4?

Plants that received inoculum from site 4 had significantly stronger indicators of ‘plant health.’

Site 3 (which presumably had the same burn frequency as site 4), had significantly worse plant outcomes.

A random forest model, using bacterial community found that increased relative abundance of the following taxa were significantly predictive of site 4:

This effect of “Site 4” was most pronounced on Leaf Number. Looking directly at that, a similar random forest model can be used to see what taxa specifically are associated with increased leaf number, regardless of inoculum source.

Again, we see Rhizobium taxa, but also note that Phenylobacterium turns up as an important player.


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A differential abundance analysis at the genus level shows other bacterial taxa which have significant deviations from the “Sterile Inoculum” samples.

We can look closer at significant taxa of interest such as Rhizobium and Phenylobacterium which turned up as important in the random forest models:



Fungi (ITS2)

This effect of “Site 4” was most pronounced on Leaf Number. Looking directly at that, a similar random forest model can be used to see what taxa specifically are associated with increased leaf number, regardless of inoculum source.


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A differential abundance analysis at the genus level shows other fungal taxa which have significant deviations from the “Sterile Inoculum” samples.



Soil properties influence on microbiome and plant performance

Overview of soil properties for each inoculum source:

Testing for significant differences between the samples…

Here are the soil properties that differ significantly between inoculum sources. Sterile inoculum soils are set as the intercept. The stats below pretty nicely reflect the obvious patterns in the overall figure.
term estimate std.error statistic p.value
Mn 1.248 0.268 4.658 0.000
NH4 0.770 0.268 2.875 0.004
Fe:inoc_site1 1.516 0.328 4.618 0.000
K:inoc_site1 0.904 0.328 2.753 0.006
NO3:inoc_site1 -0.793 0.328 -2.417 0.016
TKN:inoc_site1 0.735 0.328 2.240 0.026
Zn:inoc_site1 1.160 0.328 3.536 0.000
Fe:inoc_site2 0.831 0.328 2.532 0.012
K:inoc_site2 0.801 0.328 2.440 0.015
Mn:inoc_site2 -1.081 0.328 -3.294 0.001
TKN:inoc_site2 0.660 0.328 2.010 0.045
Mn:inoc_site3 -1.526 0.328 -4.648 0.000
NH4:inoc_site3 -0.976 0.328 -2.973 0.003
Zn:inoc_site3 -0.693 0.328 -2.112 0.035
C:N:inoc_site4 -0.893 0.328 -2.721 0.007
EC:inoc_site4 -1.026 0.328 -3.127 0.002
Mn:inoc_site4 -1.349 0.328 -4.110 0.000
B:inoc_site5 1.369 0.328 4.171 0.000
Ca:inoc_site5 1.192 0.328 3.631 0.000
K:inoc_site5 0.683 0.328 2.080 0.038
Mg:inoc_site5 0.682 0.328 2.078 0.039
Mn:inoc_site5 -1.099 0.328 -3.350 0.001
NO3:inoc_site5 1.854 0.328 5.649 0.000
TKN:inoc_site5 1.022 0.328 3.114 0.002
Zn:inoc_site5 1.448 0.328 4.412 0.000
B:inoc_site6 0.918 0.328 2.798 0.005
Mn:inoc_site6 -1.535 0.328 -4.678 0.000
NH4:inoc_site6 -0.754 0.328 -2.299 0.022
NO3:inoc_site6 1.273 0.328 3.880 0.000
Zn:inoc_site6 1.290 0.328 3.932 0.000